---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CoNLL2003_NER_BERT_Base_Cased
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9249506254114549
    - name: Recall
      type: recall
      value: 0.9458094917536183
    - name: F1
      type: f1
      value: 0.9352637710101515
    - name: Accuracy
      type: accuracy
      value: 0.9854594690057102
---

# CoNLL2003_NER_BERT_Base_Cased

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0571
- Precision: 0.9250
- Recall: 0.9458
- F1: 0.9353
- Accuracy: 0.9855

## Model description

BERT Base Cased Model available at: [bert-base-cased](https://huggingface.co/bert-base-cased)

## Intended uses & limitations

Named Entity Recognition Task (English)

## Training and evaluation data

CONLL2023 Dataset available at: [conll2003 dataset](https://huggingface.co/datasets/eriktks/conll2003)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.298         | 1.0   | 220  | 0.0792          | 0.8463    | 0.8995 | 0.8721 | 0.9769   |
| 0.0647        | 2.0   | 440  | 0.0617          | 0.9088    | 0.9362 | 0.9223 | 0.9830   |
| 0.0394        | 3.0   | 660  | 0.0574          | 0.9207    | 0.9443 | 0.9324 | 0.9846   |
| 0.0286        | 4.0   | 880  | 0.0559          | 0.9195    | 0.9438 | 0.9315 | 0.9855   |
| 0.0222        | 5.0   | 1100 | 0.0571          | 0.9250    | 0.9458 | 0.9353 | 0.9855   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1